In [2]:
data = pd.read_csv("Salary_Data.csv")
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data
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In [6]:
# see the pre-defined styles provided.
plt.style.available
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In [7]:
# use the 'seaborn-colorblind' style
plt.style.use('fivethirtyeight')
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plt.scatter(data.YearsExperience, data.Salary)
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In [12]:
sns.lmplot(x = "YearsExperience", y = "Salary", data = data, size = 7)
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In [36]:
X
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In [37]:
X = data.iloc[:,0].values
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y = data.iloc[:,1].values
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from sklearn.model_selection import train_test_split
In [52]:
X_train,X_test, y_train, y_test = train_test_split(X,y, test_size = 1/3, random_state = 0)
Linear Regression¶
In [53]:
from sklearn.linear_model import LinearRegression
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model = LinearRegression()
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model.fit(X_train.reshape(-1,1),y_train)
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In [56]:
y_pred = model.predict(X_test.reshape(-1,1))
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y_pred
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In [58]:
y_test
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In [68]:
plt.figure(figsize=(10,8))
sns.regplot(X_train,y_train, label="Train set")
plt.scatter(X_test,y_test, c="orange", label = "Test set")
plt.legend()
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